Attributed graph mining in the presence of automorphism

• Published in: Knowledge and information systems 2017-02, Vol.50 (2), p.569-584
• Main Authors: Pasquier, Claude, Flouvat, Frédéric, Sanhes, Jérémy, Selmaoui-Folcher, Nazha
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Combinatorial analysis; Combinatorics; Computer Science; Data mining; Data Mining and Knowledge Discovery; Data Structures and Algorithms; Database Management; Explosions; Genes; Graph theory; Graphs; Information Storage and Retrieval; Information systems; Information Systems and Communication Service; Information Systems Applications (incl.Internet); Isomorphism; IT in Business; Pattern recognition; Regular Paper; Studies; Tasks
• ISSN: 0219-1377; 0219-3116
• DOI: 10.1007/s10115-016-0953-9

Attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.